The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-16 DOI:10.1186/s12880-024-01414-1
He-Xin Liang, Zong-Ying Wang, Yao Li, An-Ning Ren, Zhi-Feng Chen, Xi-Zhen Wang, Xi-Ming Wang, Zhen-Guo Yuan
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Abstract

To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.
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基于多模态磁共振成像的支持向量机模型在预测胶质瘤中 IDH-1 突变和 Ki-67 表达中的应用价值
研究基于扩散加权成像(DWI)、动态对比增强成像(DCE)和酰胺质子转移加权成像(APTW)的支持向量机(SVM)模型在预测胶质瘤中异柠檬酸脱氢酶1(IDH-1)突变和Ki-67表达方面的应用价值。回顾性分析了309例经病理证实的胶质瘤患者的DWI、DCE和APTW图像,并将其分为IDH-1组(IDH-1(+)组和IDH-1(-)组)和Ki-67组(低表达组(Ki-67≤10%)和高表达组(Ki-67>10%))。所有病例按照 7:3 的比例分为训练集和验证集。训练集用于选择特征和建立机器学习模型。通过特征选择后的数据建立 SVM 模型。在 IDH-1 组和 Ki-67 组中建立了四个单一序列模型和一个组合模型。接收器操作者特征曲线(ROC)用于评估模型的诊断性能。验证集数据用于进一步验证。在 IDH-1 组和 Ki-67 组中,联合模型的预测效率均优于单一序列模型,但单一序列模型的预测效率更高。在 Ki-67 组中,组合模型是由六个选定的放射组学特征建立的,在训练集和验证集中的 AUC 分别为 0.965 和 0.931。在 IDH-1 组中,综合模型由四个选定的放射组学特征建立,训练集和验证集的 AUC 分别为 0.997 和 0.967。通过DWI、DCE和APTW图像建立的放射组学模型可用于胶质瘤患者术前IDH-1突变和Ki-67表达的检测。基于组合序列的放射组学模型的预测性能优于单一序列模型。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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